The present invention relates to a method and system for classification and quantitative evaluation of masses, and more particularly, to a method and system for automatic, and therefore objective, classification and quantitative evaluation of adnexal masses based on cross-sectional or projectional images of the adnex.
As used herein in the specification and in the claims section hereinunder, the terms “adnex” and “adnexal” refer to the pelvic gynecological adnex, which is also known in the art as the uteral adnex, including the ovary and the fallopian tubes complex.
Ovarian masses are a common phenomenon among women of all ages. The necessity to find an efficient way for classifying ovarian masses and detecting malignant tumors is evident, especially considering the high mortality rate due to ovarian cancer and the difficulty in detecting a tumor in the early stages of the disease. In order to quantitatively assess the malignancy of an ovarian pathology, it is common to score several properties of the ovarian mass (obtained from ultrasound images) according to a pre-determined table, and to use the resulting value for classification. Currently, no existing scoring system is based on either automatic or semi-automatic image analysis.
Major types of ovarian masses: Cysts are the most common ovarian pathology. Most of them are benign, however some cysts are malignant. An ovarian cyst is formed when part of the ovary is filled with fluid while the ovarian tissue is compressed to the remaining volume. Since the fluid within the cyst is not echogenic, while the ovarian tissue is echogenic, the cyst in its simplest form appears in an ultrasound image as a dark region encircled by bright pixels. Cysts, however, are usually more complicated and are typically characterized by several features. To start with, the fluid within the cyst may be clear or turbid. Cysts may also contain small regions of ovarian tissue penetrating from the cyst's boundary into its volume. Such projections are called papillations. The thickness of the wall, i.e., the layer between the cyst and the external ovarian boundary (containing ovarian tissue), is also an important parameter. The cyst may be divided into several parts to form a multilocular cyst by septations, which are narrow stripes of ovarian tissue. These septations can be either complete (thus forming several separated cystic lumens) or incomplete. The size of the cyst and the regularity (i.e., smoothness) of its wall are also important for diagnosis.
Solid masses are another class of ovarian pathologies. Although some of them are benign (e.g., solid teratoma, fibroma), many of them are malignant. Contrary to cysts, solid masses appear as lumps of echogenic (i.e., bright) material within the ovary. Important parameters besides their size are the homogeneity (as appears in the ultrasound image) of the solid material, and the presence of echogenic foci (small very bright spots). Solid masses are less detectable than cysts because of the low contrast between the mass and its surroundings. There are cases in which the ovarian mass encompasses a cyst and solid tissue. Such cases are defined as semi-solid masses, which are also known as complex masses.
The above partition of ovarian masses into three major types is used by Fleischer [1] for the sonographic differential diagnosis of pelvic masses. This partition, and the morphological features used for describing the mass are provided in Table 1, below.
TABLE 1Rough classification of ovarian massesTypes of ovarian massesCyst, semi-solid, solidFeature setEchogenity (gray level)Wall structure (papillations)SeptationsWall thicknessMass volumeEchogenic foci
Based on this set of characteristics, a detailed diagnosis of the mass (identifying the specific pathology and assessing its malignancy), is performed. Obviously, the full medical decision making process is based not only on the morphological features as observed in B-scan ultrasound images, but also on blood flow features as expressed by a Doppler signal (e.g., RI, PI), blood tests (e.g., checking the serum CA125 levels), the patient's history, and histological examination.
Scoring systems: A commonly used tool for malignancy detection in the ovary are scoring systems. The idea underlying the scoring system is to score several properties of the ovarian mass, according to a pre-determined table, and to use the resulting value (such as the sum of the individual scores) for classification. The scoring table usually provided a small number of different scores for each property, where the division to values is determined by commonly used criteria. Sassone et al. [2] designed a scoring system that includes four characteristics of the mass (the inner wall structure, the wall thickness, the presence and width of septa, and the echogenity of the region). Other scoring systems were suggested [3-6]. The scoring system discussed by DePriest et al. [5], for example, refers to the volume, the wall structure (smooth or including papillations), and the structure of the septa.
Although these scoring systems reasonably succeed in sorting the ovarian mass to either benign or malignant, they are based on subjective evaluations made by an operator. Despite the recent progress in image processing, no existing scoring system is currently based either on automatic or on semi-automatic analysis of images.
Automatic analysis of ovarian masses—current status: The major attempts to automatically analyze ovarian ultrasound images [7-12] referred only to ovarian follicles. A computerized system for quantification of ovarian masses, however, must deal with more complex ovarian morphology. Currently, no published research fully addresses this problem. A powerful tool in the area of medical diagnosis are expert systems. Brüning et al. [13] presented an expert system called ADNEXPERT, which is specifically designed to assist the sonographic diagnosis of adnexal tumors. However, the ADNEXPERT system provides differential diagnosis of adnexal masses based on classification data provided manually thereto by the physician. Thus, the ADNEXPERT system provides automation clearly limited to the final decision making step of mass evaluation.
There is thus a widely recognized need for, and it would be highly advantageous to have, a method and system for automatic classification and quantitative evaluation of adnexal masses based on cross-sectional or projectional images of the adnex, which provide an objective scoring system for adnexal masses.